Open Access

ARTICLE

Multifactorial Disease Detection Using Regressive Multi-Array Deep Neural Classifier

by D. Venugopal¹, T. Jayasankar^2,*, N. Krishnaraj³, S. Venkatraman⁴, N. B. Prakash⁵, G. R. Hemalakshmi⁵

1 Department of Electronics and Communication Engineering, KPR Institute of Engineering and Technology, Coimbatore, India
2 Department of Electronics and Communication Engineering, University College of Engineering, BIT Campus, Anna University, Tiruchirappalli, India
3 School of Computing, SRM Institute of Science and Technology, Kattankulathur, 603203, India
4 School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India
5 Department of Electrical and Electronics Engineering, National Engineering College, Kovilpatti, India

* Corresponding Author: T. Jayasankar. Email:

Intelligent Automation & Soft Computing 2021, 28(1), 27-38. https://doi.org/10.32604/iasc.2021.015205

Received 10 November 2020; Accepted 27 December 2020; Issue published 17 March 2021

Abstract

Comprehensive evaluation of common complex diseases associated with common gene mutations is currently a hot area of human genome research into causative new developments. A multi-fractal analysis of the genome is performed by placing the entire DNA sequence into smaller fragments and using the chaotic game representation and systematic methods to calculate the general dimensional spectrum of each fragment. This is a time consuming process as it uses floating point to represent large data sets and requires processing time. The proposed Regressive Multi-Array Deep Neural Classifier (RMDNC) system is implemented to reduce the computation time, it is called a polymorphic processor, the system design a dedicated processor, based on a hardware-oriented algorithm that we have proposed to efficiently compute the general dimensional spectrum of DNA sequences. The proposed Regressive Multi-Array Deep Neural Classifier (RMDNC) system concept of the biology information is classified as follows. Protein-Protein Interaction (PPI) networks explain the understanding of organisms in coronary arteries, genetics, gender studies, cardiovascular risk factors and atherosclerosis, the development and identification of carotid intimal media thickness Pay particular attention to arterial calcification, which is an important factor in improving. Also, multiple biological activities of the human body are responsible for these interactions. In this work, computational studies have been completed to understand the PPI network of obstructive sleep apnea, cardiovascular disease, stroke and epilepsy.

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Keywords

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